Five Trends in Predictive Analytics

Predictive analytics, a technology that has been around for decades has gotten a lot of attention over the past few years, and for good reason. Companies understand that looking in the rear-view mirror is not enough to remain competitive in the current economy. Today, adoption of predictive analytics is increasing for a number of reasons including a better understanding of the value of the technology, the availability of compute power, and the expanding toolset to make it happen. In fact, in a recent TDWI survey at our Chicago World Conference earlier this month, more than 50% of the respondents said that they planned to use predictive analytics in their organization over the next three years. The techniques for predictive analytics are being used on both traditional data sets as well as on big data.

Here are five trends that I’m seeing in predictive analytics:

Ease of use. Whereas in the past, statisticians used some sort of scripting language to build a predictive model, vendors are now making their software easier to use. This includes hiding the complexity of the model building process and the data preparation process via the user interface. This is not an entirely new trend but it is worth mentioning because it opens up predictive analytics to a wider audience such as marketing. For example, vendors such as Pitney Bowes, Pegasystems, and KXEN provide solutions targeted to marketing professionals with ease of use as a primary feature. The caveat here, of course, is that marketers still need the skills and judgment to make sure the software is used properly.